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Feature/lee1043 mov modularize #1060

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Feb 22, 2024
Merged

Feature/lee1043 mov modularize #1060

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426e7fc
logic simplified
lee1043 Feb 7, 2024
8d4f9de
clean up
lee1043 Feb 8, 2024
6bc7232
update
lee1043 Feb 12, 2024
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31 changes: 15 additions & 16 deletions pcmdi_metrics/variability_mode/lib/eof_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -396,10 +396,10 @@ def get_anomaly_timeseries(ds: xr.Dataset, data_var: str, season: str) -> xr.Dat
raise TypeError(
"The first parameter of get_anomaly_timeseries must be an xarray Dataset"
)
# Get anomaly field
# Get anomaly field by removing annual cycle
ds_anomaly = ds.temporal.departures(data_var, freq="month", weighted=True)
# Get temporal average if needed
if season == "yearly":
# remove seasonal cycle
ds_anomaly = ds.temporal.departures(data_var, freq="month", weighted=True)
# yearly time series
ds_anomaly = ds_anomaly.temporal.group_average(
data_var, freq="year", weighted=True
Expand All @@ -410,27 +410,26 @@ def get_anomaly_timeseries(ds: xr.Dataset, data_var: str, season: str) -> xr.Dat
ds_ave = ds_anomaly.temporal.average(data_var)
# anomaly
ds_anomaly[data_var] = ds_anomaly[data_var] - ds_ave[data_var]
else:
# Remove annual cycle
ds_anomaly = ds.temporal.departures(data_var, freq="month", weighted=True)
if season != "monthly":
ds_anomaly_all_seasons = ds_anomaly.temporal.departures(
data_var,
freq="season",
weighted=True,
season_config={"dec_mode": "DJF", "drop_incomplete_djf": True},
)
ds_anomaly = select_by_season(ds_anomaly_all_seasons, season)
elif season.upper() in ["DJF", "MAM", "JJA", "SON"]:
ds_anomaly_all_seasons = ds_anomaly.temporal.departures(
data_var,
freq="season",
weighted=True,
season_config={"dec_mode": "DJF", "drop_incomplete_djf": True},
)
ds_anomaly = select_by_season(ds_anomaly_all_seasons, season)
# return result
return ds_anomaly


def select_by_season(ds: xr.Dataset, season: str) -> xr.Dataset:
time_key = get_time_key(ds)
lat_bnds_key = get_latitude_bounds_key(ds)
lon_bnds_key = get_longitude_bounds_key(ds)
ds_subset = ds.where(ds[time_key].dt.season == season, drop=True)
# Preserve original spatial bounds info
# Extract original bounds
lat_bnds_key = get_latitude_bounds_key(ds)
lon_bnds_key = get_longitude_bounds_key(ds)
# Assign back to the new dataset
ds_subset[lat_bnds_key] = get_latitude_bounds(ds)
ds_subset[lon_bnds_key] = get_longitude_bounds(ds)
return ds_subset
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6 changes: 3 additions & 3 deletions pcmdi_metrics/variability_mode/variability_modes_driver.py
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Original file line number Diff line number Diff line change
Expand Up @@ -304,10 +304,10 @@
debug=debug,
)

# Save global grid information for regrid below
# Get global grid information for later use: regrid
ref_grid_global = get_grid(obs_timeseries)

# Declare dictionary variables to keep information from observation
# Set dictionary variables to keep information from observation in the memory during the season and model loop
eof_obs = {}
pc_obs = {}
frac_obs = {}
Expand All @@ -317,7 +317,7 @@
stdv_pc_obs = {}
obs_timeseries_season_dict = {}

# Dictonary for json archive
# Set dictonary for json archive
if "obs" not in result_dict["REF"]:
result_dict["REF"]["obs"] = {}
if "defaultReference" not in result_dict["REF"]["obs"]:
Expand Down
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